New therapy and medical test (hereafter "intervention") development is extremely speculative. In order to bring a new intervention to market, numerous hurdles must be overcome. Each hurdle involves gaining knowledge about how the intervention works, under what situations it works, and whether or not it is safe. The major hurdles in development are discovering a proposed intervention, testing it in a human population, determining whether its effect produces a significant improvement over other interventions for a given disease, and finally, educating practitioners and patients about its benefit and appropriate use. Each of these hurdles requires the generation, collection, and analysis of a large amount of data to test hypotheses about the proposed intervention, i.e., whether or not it is effective; for which patients it is most effective; and whether or not it is an improvement over standard interventions for the same disease. The system described in this application was developed to help researchers achieve each of these major hurdles.
The development of interventions consists of four identifiable stages: target discovery, clinical trial design, pharmacoeconomic assessment, and product distribution/use. Target discovery is the process of finding a biological or cellular mechanism in the biology of the disease process that, if affected or known through testing, alters the course of disease progression. Target discovery identifies both the particular target in the disease biology and the intervention that affects or identifies the target. The pathology of a disease is often so complex that it takes years of research to discover a target leverage point that provides a cure or at least relieves the symptoms. This is clearly one of the most difficult problems facing pharmaceutical research. It is a very labor-intensive and time-consuming stage in which a positive outcome is not assured. It relies on discovering an insight, which happens in due course rather than on a fixed schedule.
Current approaches to target discovery concentrate on standard laboratory experimentation to generate hypotheses and animal trials to further evaluate those hypotheses. These standard approaches are often limited by the knowledge and understanding that the researchers have of the disease biology. Researchers bring to the design of their studies a paradigm or guiding theory that directs the questions they seek to answer. While this top-down approach to target discovery can be very successful if the theory is good, it begs the question of how to develop a theory in the first place. In the absence of a guiding theory, researchers must cull through large bodies of data to develop an initial insight. There are few tools and no standard approaches that support this bottom-up, or data-driven approach to target discovery and the identification of proposed interventions.
The next stage in intervention development involves designing and conducting formal clinical trials of the proposed intervention. Clinical trials typically isolate narrowly on a single variable, e.g., the proposed intervention, and use a control group as a baseline from which the variable is measured. Observations from a clinical trial attempt to draw conclusions from statistical differences between the control and experimental groups. Because of the enormous expense of conducting trials large enough to statistically assess a broad range of variables, these observations often fail to take into account the multivariate, dynamic nature of the patients individually or as a group.
Clinical trials are very data intensive, time-consuming and costly. The goal is to gather enough evidence to support the claims of the intervention's efficacy and to obtain regulatory approval. A typical cycle for a clinical trial may take several years. For example, designing the trial may take six months, performing the trial may take a year, and analyzing the results may take yet another six months. After years of testing, the results may still be unexpected or difficult to interpret.
The design of a clinical trial is limited by the researchers' knowledge of the underlying disease process, how patient attributes affect it, and how the proposed intervention, the disease biology, and the patient attributes interact. Without this knowledge, designers might test patient types for which the intervention is ineffective or has adverse effects. Additionally, they might design an inappropriate regimen for delivering the proposed intervention. Either of these alternatives could lead the research team to conclude that a proposed intervention has no effect, when in fact it is very effective for the right patients with the correct delivery schedule. Alternatively, without this knowledge, a positive clinical trial might lead the research team to conclude that a proposed intervention has a profound effect without a full understanding of the possible limitations.
Much research is underway to develop tools to support the clinical trial design process. Most of these tools concentrate on analyzing the merit of alternative designs given the researchers' assumptions about the pharmacokinetics and pharmacodynamics of an intervention. Given the appropriate assumptions, these tools help the researchers assess the risks of the clinical trial design, select the appropriate dose requirements, and reveal the statistical characteristics of the proposed study. Thus, researchers must have considerable prior knowledge of the intervention effects, including knowledge of the effects of the intervention on the disease biology and of the efficacy of alternative regimens for delivering the intervention at the biological level. They take the effects of the intervention on different patient types as a given, and proceed to evaluate competing clinical trial designs for their statistical power. Again, this approach is based only on a top-down methodology that does not support clinical trial design by exploration of biological effects of a proposed intervention on patient types. No currently available tools support the development of clinical trial design by a data driven analysis of the patient attributes which are efficaciously effected by a proposed intervention.
The third stage in the development of interventions, pharmacoeconomic analysis, involves analyzing the benefits of the proposed intervention relative to standard, existing interventions. The pharmaceutical industry is still grappling with how to adequately evaluate the pharmacoeconomic benefit of a potential product and there are no established methods for conducting pharmacoeconomic analysis. Many pharmaceutical companies, as well as the FDA, recognize the need to establish standard procedures for generating claims about the relative effectiveness of competing products, but the methodologies that have been used are extremely expensive, involving comparative clinical trials. To date, methods of evaluating relative clinical outcomes and quantifying quality of life differences between competing intervention scenarios have not been rigorously formalized. The computational methodologies that do exist involve mining large databases of clinical use data to find patterns that can support effectiveness claims. However, these are post hoc approaches; there are no standards for estimating pharmacoeconomic value during the intervention development process. As a result, companies may invest a large amount of money bringing a product to market that cannot achieve an adequate market share to justify the development expense.
The final stage in the development of interventions is product distribution and use. This process involves bringing knowledge and information about the new intervention to the practitioners and patients in order to educate them about the processes underlying the disease, the expected changes in the patient's manifestation of the disease over time (i.e., the disease progression), and the effects of alternative interventions in the disease progression and the patient's overall outcome, including the patient's resulting quality of life and cost. This process draws on the data that supported the target discovery, clinical trials, and pharmacoeconomic analyses to help practitioners and patients make informed decisions about the use of the product.
Traditional approaches to product distribution include developing brochures and pamphlets that present the benefits of the new product and discuss its use. These approaches emphasize the new product and seldom offer unbiased comparisons to existing methods and practices. In addition, companies seldom develop materials to support patient education. However, automated support for practitioner education that clearly presents the disease progression over time for specific patient attributes, and further shows the benefits and limitations of a new intervention is not now currently available. Without automated support for this process that combines and synthesizes all sources of existing data into a meaningful clinical interpretation of estimated disease progression for a specific patient over time and that provides a comparison with existing intervention practices, companies are handicapped in their ability to explain the benefits of their new intervention to potential users and to indicate when it is most effective. Thus, it is difficult to bring the new product to the appropriate constituencies.
A need clearly exists to support, speed, and improve the four major stages of developing disease interventions. The present invention overcomes prior limitations by supporting the collection, storage, and analysis of the data targeted at each of the major hurdles in the development process from discovery to commercialization. The outcome achieved by the present invention aids the discovery of proposed interventions to support therapies and/or medical tests, the design of relevant clinical trials for the proposed intervention, a comparison of the benefits of the proposed intervention to existing practices, and the education of patients and practitioners in the appropriate use of the new intervention to support product commercialization.